Advisor

Alexander J. Hunt

Date of Award

7-16-2018

Document Type

Thesis

Degree Name

Master of Science (M.S.) in Mechanical Engineering

Department

Mechanical Engineering

Physical Description

1 online resource (viii, 48 pages)

Abstract

Human balance control is a complex feedback system that must be adaptable and robust in an infinitely varying external environment. It is probable that there are many concurrent control loops occurring in the central nervous system that achieve stability for a variety of postural perturbations. Though many engineering models of human balance control have been tested, no models of how these controllers might operate within the nervous system have yet been developed. We have focused on building a model of a proprioceptive feedback loop with simulated neurons. The proprioceptive referenced portion of human balance control has been successfully modeled by a PD controller with a time delay and output torque positive feedback. For this model, angular position is measured at the ankle and corrective torque is applied about the joint to maintain a vertical orientation. In this paper, we construct a neural network that performs addition, subtraction, multiplication, differentiation and signal filtering to demonstrate that a simulated biological neural system based off of the engineering control model is capable of matching human test subject dynamics.

Persistent Identifier

https://archives.pdx.edu/ds/psu/26192

Available for download on Tuesday, July 16, 2019

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